Method for locating areas of interest of a substrate

ABSTRACT

In a method for evaluating the signal intensity of at least one area of a substrate embedded in a substrate surrounding with a background intensity by means of a computer, a scatter parameter of the substrate is determined. Further, a matrix (IS) of pixel intensities of pixels located within an evaluation window enclosing the area and surrounding is determined. The pixel intensities of the matrix (IS) are used to obtain a histogram of pixel intensities. This evaluation histogram shows two distribution peaks, the first distribution peak with the lowest intensity corresponding with the surrounding pixels and the second distribution peak with the highest intensity corresponding with the area pixels. A curve with two peaks is fitted on this evaluation histogram. The scatter parameter is used to correct for scattering either the matrix (IS) or the fitted curve. The signal intensity of pixels in the area is determined by means of data obtained from the curve fitted on tile pixel intensity histogram.

[0001] The present invention relates to a method for evaluating thesignal intensity of at least one area of a substrate embedded in asubstrate surrounding with background intensity by means of a computer,and to a method for locating possible areas of interest of a substrateembedded in a substrate surrounding.

[0002] U.S. Pat. No. 5,795,716 discloses a method of this type which isused in a computer-aided visualisation and analysis system for sequenceevaluation. Generally the substrate comprises an array of areas, eacharea having a known binding substance or probe, capable of specificallybinding with an analyte. Assays in which an array can be used mayinclude sequencing by hybridization, immunoassays, receptor/ligandassays etc. For example, the array may be used to screen a biologicalsample, such as blood for the presence of a large number of analytes. Ifthe substrate is brought into contact with a liquid that contains one ormore analytes, a reaction pattern may occur representing the specificaffinity of the analytes(s) for the binding substances of the array. Thearray may consist of areas comprising nucleic acid probes. The array maybe used for the detection and/or typing of viral or bacterial nucleicacid or for mutation detection. Likewise an array can be used forperforming immunoassays. In that case the binding substances or probesmay be antigens (peptides) or antibodies. By binding of an analyte tothe binding substance a detectable signal, such as a fluorescent signal,is generated, for example, by binding with a second, labeled reagent.For instance, a scanner generates an image file and this image file isevaluated to determine the signal intensity of each area. To obtainaccurate information, it is very important to accurately determine thesignal intensity of the area where a binding substance is located. Inthe known method, this signal intensity is determined using a backgroundintensity of a “blank” area, wherein is it assumed that the blank areaprovides a background intensity only. However, it is very difficult tofind an area on the substrate providing with certainty backgroundintensity only. Further, in many of the known methods one backgroundintensity determined in this manner is used for all areas so that anyvariations in background intensity along the surface of the substrateare not considered. Moreover, any effects on the signal intensitymeasured caused by scattering in the substrate is not considered at all.

[0003] The invention aims to provide an improved method of theabove-mentioned type.

[0004] According to the invention a method for evaluating the signalintensity of at least one area of a substrate embedded in a substratesurrounding with a background intensity by means of a computer, ischaracterized in that a scatter parameter of the substrate isdetermined, and in that a matrix (I_(S)) of pixel intensities of pixelslocated within an evaluation window enclosing the area and surroundingis determined, wherein the pixel intensities of the matrix (I_(S)) areused to obtain an evaluation histogram of pixel intensities, saidevaluation histogram showing two distribution peaks, the firstdistribution peak with the lowest intensity corresponding with thesurrounding pixels and the second distribution peak with the highestintensity corresponding with the area pixels, wherein a curve with twopeaks is fitted on said evaluation histogram, wherein the scatterparameter is used to correct for scattering either the matrix (I_(S)) orthe curve, wherein the signal intensity of pixels in the area isdetermined by means of data obtained from the curve fitted on saidevaluation histogram.

[0005] In this manner a method is obtained, wherein the mean signalintensity of pixels in the area is determined with increased accuracy bycorrecting the pixel intensities of both the area and surrounding forthe scatter characteristics of the substrate. In the method of theinvention evaluation of the background intensity is an inherent part ofthe evaluation of the signal intensity of the area of interest. Therebyseparate steps for locating a part of the substrate for determining thebackground intensity are not required and, moreover, the backgroundintensity of each area is determined at a location close to thecorresponding area so that variations in background intensity along thesurface of the substrate do not affect the evaluation result.

[0006] The invention further provides a method for locating possibleareas of interest of a substrate embedded in a substrate surrounding bymeans of a computer, characterized in that an image file of thesubstrate is processed in a low pass filter algorithm to determine amatrix of local mean values for all pixels of the image file, whereinthe matrix of local mean values is combined with the matrix of actualpixel values of the image file to obtain a first matrix of high/lowpixel values which are either high or low depending on the actual pixelvalue being above or below the corresponding local mean value, whereinthe high/low pixel values of the first matrix are further processed in amedian filter algorithm to obtain a second matrix of median pixelvalues, wherein each median pixel value equals the majority of thehigh/low pixel values of the first matrix, wherein the median pixelvalues are processed row by row and column by column to determine themean row values and mean column values, respectively, wherein the rowsand columns with the highest mean row values and highest mean columnvalues are selected as estimates of the row centre lines and columncentre lines of possible areas of interest, the intersections of whichare the centres of possible areas of interest.

[0007] The invention will be further explained by reference to thedrawings showing some diagrams to illustrate two embodiments of themethod of the invention.

[0008]FIG. 1 shows an image used to determine a scatter parameter of thesubstrate.

[0009]FIG. 2 shows the scatter decay function obtained form the image ofFIG. 1.

[0010]FIG. 3 shows an image of a substrate with an array of 6×4 areas,each area having a known probe and after deposition of a labelledmaterial on the substrate.

[0011]FIG. 4 shows the image of FIG. 3 after mathematically removing thescatter effects.

[0012]FIG. 5 shows an evaluation window on one of the areas of FIG. 4.

[0013]FIG. 6 shows the evaluation histogram of a matrix of deconvolutedpixel intensities together with two fitted curves.

[0014]FIGS. 7 and 8 illustrate the determination of a theoreticalscatter histogram used for fitting in a second embodiment of the methodof the invention.

[0015]FIG. 9 shows the evaluation histogram obtained from the pixelintensities of the matrix I_(S) of an area of FIG. 3 together with afitted theoretical histogram obtained in the manner as shown in FIGS. 7and 8.

[0016] FIGS. 10A-10D and 11A-11F show steps in a preferred embodiment ofa method of the invention to evaluate an image file of a substrate asshown in FIG. 3 to locate possible areas of interest.

[0017] The method of the invention for evaluating the signal intensityof an area of a substrate embedded in a substrate surrounding isimplemented as a computer program for use in a computer. The method canbe used for example for sequence evaluation by analysing the signalintensities of hybridised nucleic acid probes. However, as mentionedabove the substrate can be used for any other assay, such asimmunoassays, receptor/ligand assays etc. As the present method onlyrelates to evaluating the signal intensity, the manner for sequenceevaluation will not be described in further detail. Reference is made toWO 9902266 for example.

[0018] In such a sequence evaluation, a substrate as described in WO9902266 can be used for example, having in the case shown in FIG. 3 anarray of 6×4 areas 1, each area or dot having a probe with a knownsequence. When a material with a fluorescence label is contacted withthe surface of the substrate, different concentrations of the labelledmaterial will be found in the different dot areas. Illumination of thesubstrate will yield fluorescence light originating from each dot area 1and FIG. 3 shows an image obtained by means of a CCD camera. It is notedthat although in this case fluorescence light is obtained, the method isnot restricted to evaluating fluorescence intensities. The methodencompasses evaluating any type of signal intensity originating from anarea embedded in a surrounding. The image as shown in FIG. 3 is storedin the computer, for example on hard disk, as an image file. As can beseen in FIG. 3, there is no sharp transition from a dot area 1 to itssurrounding so that it is difficult to find a location for determiningthe background intensity in the vicinity of a dot area 1. The gradual orblurred transition from dot area to surrounding is caused by scatteringof the fluorescent light of the labelled material. In order to correctfor this scattering effect, a scatter parameter of the substrate isdetermined in the method of the invention.

[0019]FIG. 1 shows by way of example a manner of determining a scatterparameter of the substrate. FIG. 1 is an image of a sidewise illuminatedsubstrate having a thickness of 60μ in this example. In FIG. 1 a line 2is shown along which the pixel intensities are determined. It is notedthat the wording “pixel intensity” is used to indicate the signalintensity obtained from one pixel of the CCD camera or any other type ofimaging device used to provide the image of the array. From the pixelintensities measured along this line 2 an exponential scatter functioncan be made as shown in the diagram of FIG. 2 having in this case acharacteristic decay of 33,8μ. This characteristic scatter decay is usedin the method of the invention to correct the pixel intensities measuredfor the scatter effects of the substrate. As the scatter decaydetermined in this manner is measured by sidewise illumination of thesubstrate, any illumination not scattered and going directly towards theimaging device used in normal measuring circumstances is not taken intoaccount. To correct for such signal intensity originating directly fromthe substrate without scattering a predetermined percentage of thereceived signal intensity for all pixels can be deducted for example.

[0020] It is noted that in FIG. 1 a dry substrate is measured. Ofcourse, scatter parameters of the same substrate for a wet state can bedetermined in a corresponding manner. Further scatter parameters fordifferent types of substrates can be determined and stored in thecomputer. In the evaluation method, a stored scatter parameter can beselected from the memory of the computer in accordance with the type andstate of the substrate used. It is also possible to enter a knownscatter parameter of the substrate used through a suitable input device.In this respect it is noted that the step of determining a scatterparameter of the substrate encompasses any manner of input of apreviously determined scatter parameter for use in the method described.

[0021] The dot areas 1 are evaluated dot by dot. For this evaluation onedot area 1 is isolated with its direct surrounding from the remainder ofthe substrate image by means of an evaluation window 3 which is shown inFIG. 5. The size of the evaluation window 3 is such that the completedot area 1 and its direct surroundings are located within the evaluationwindow 3. In the example of FIG. 5, the window 3 comprises 55×55 pixels.In isolating the dot areas 1, the computer program can first evaluatethe complete image of FIG. 3 to locate the centre of each dot area 1 andto determine the size of the evaluation window 3 to be used. By means ofa suitable user interface the location of the centre of each dot areaand/or size of the window 3 can be changed. A favourable manner ofevaluating the complete image of FIG. 3 to locate the dot centres willbe described hereinafter.

[0022] The intensities of all pixels within the evaluation window 3 areinserted in the matrix I_(S) of pixel intensities, i.e. theseintensities are representing signal intensities including the scattereffects of the substrate. These pixel intensities will be referred to asscattered pixel intensities in this description. As the scatterparameter of the substrate is known, the scatter effect can bemathematically corrected. In the first embodiment of the methoddescribed, the scatter effect is taken into account by a deconvolution.Deconvolution methods as such are known in mathematics. The result ofdeconvoluting the matrix I_(S) of scattered pixel intensities with thescatter parameter of the substrate is a matrix ID of non-scattered pixelintensities, i.e. these non-scattered pixel intensities are representingsignal intensities corrected for scatter effects. FIG. 4 shows thedisplay on the computer monitor of the image file of FIG. 3 afterdeconvolution. In the display of this processed image file it can beseen that the blurring effect of scattering at the transition of dotarea and its direct surroundings is removed.

[0023] In a further step of this embodiment, the non-scattered pixelintensities of the matrix I_(D) are used to determine a pixel intensityevaluation histogram, which evaluation histogram is shown in FIG. 6.Clearly, this histogram of pixel intensities shows two distributionpeaks 4 and 5, wherein the first distribution peak 4 with the lowestintensity represents the surrounding pixels and the second distributionpeak 5 with the highest intensity represents the pixels of the dot area1.

[0024] As both these distributions 4 and 5 will generally resemblenormal distributions, a standard fitting method can be used to fit twonormal distribution curves 6 and 7 on the distribution peaks 4 and 5. Assuch fitting methods are known per se, a detailed description is deemedto be superfluous. For example a least mean square method can be used tofind the best fitting curves. In determining the normal distributioncurves 6 and 7 a noise parameter of the noise present in the pixelintensities of the matrix (I_(S)) or in all pixel intensities of thecomplete image file of the substrate of FIG. 3 is used, in particularthe standard deviation of this noise. This standard deviation σ_(noise)is determined by taking for example the difference between adjacentpixels resulting in a distribution with a mean value 0 and a standarddeviation a σ=σ_(noise)*{square root}2.

[0025] Finally, the mean value of the first fitted distribution curve 6is taken as the best estimate for the mean pixel intensity of the purebackground intensity. The mean value of the second fitted distributioncurve 7 is taken as the best estimate of the mean pixel intensity of thedot area 1, i.e. background intensity together with fluorescenceintensity of the labelled material. Therefore, the difference of bothmean values provides the mean fluorescence intensity of the pixels inthe dot area 1, i.e. of the labelled material.

[0026] In a second embodiment of the method of the invention, thescatter parameter of the substrate is taken into account in a differentmanner. For, it is known that deconvolution amplifies the noise presentin the data obtained from the CCD camera. In case of low signalintensity signals, evaluation by deconvolution yields poor results.

[0027] In accordance with the second embodiment of the invention, thescatter parameter is taken into account by determining the scatterresponse of a predetermined theoretical label area as schematicallyshown in FIGS. 7A-7C. In FIG. 7A different scatter responses are shownfor different scatter parameters, i.e. scatter parameters for differentsubstrates and different substrate conditions. The straight line at x=1corresponds with the non-scattered response curve, i.e. an intensity Iwithin the theoretical label area and an intensity zero in thesurrounding of the area. Combining this non-scattered response with thedifferent scatter parameters results in the different scatter responsesshown in FIG. 7A. It is noted that in FIG. 7A the scatter responses areshown for normalized label area and normalized intensity, i.e. with theradius R of the area and the intensity I as units. FIG. 7B shows anevaluation window of the same size as the evaluation window used in FIG.5 showing the scattered response of the theoretical label area togetherwith a corresponding scatter response curve 8. As this scatter responsecurve 8 shows the intensities along one radius from the centre of thetheoretical area up to the edge of the evaluation window, a normalizedhistogram of pixel intensities as shown in FIG. 7C can be made by usingthe pixel intensity values provided by the scatter response curve 8 andthe number of pixels surrounding the centre of the label area.

[0028]FIG. 8 shows two next steps in the second embodiment of the methodof the invention. According to FIG. 8A the normalized histogram of FIG.7C is scaled by choosing a specific background intensity I_(B) and areaintensity I_(A) and this scaled histogram is convoluted with the noisedistribution parameter obtained in the above-described manner. In thismanner a theoretical scatter histogram is obtained and this theoreticalscatter histogram shows two distribution peaks 9 and 10 representing thebackground intensity caused by scattering and the area intensity. Thetheoretical scatter histogram is fitted on an evaluation histogramobtained from the scattered pixel intensities of the matrix I_(S).Standard fitting methods can be used to find the best fittingtheoretical scatter histogram by varying at least the backgroundintensity I_(B) and area I_(A) intensity values. As a furtheralternative it is possible to also vary the radius value of thetheoretical area, the standard deviation of the noise and the scatterparameter value used.

[0029]FIG. 9 shows the evaluation histogram obtained from the scatteredpixel intensities of the matrix I_(S) with the best fitting theoreticalscatter histogram. When the best fitting theoretical scatter histogramcurve is found, the corresponding background intensity and area pixelintensity values are used as best estimates for the background intensityand area pixel intensity. The difference of area pixel intensity andbackground intensity corresponds with the fluorescence intensity of thepixels in the dot area 1.

[0030] It is noted that in the above examples of the method describedonly the mean value of the peaks of the fitted curve are used forevaluation. The fitted curve comprises further information which can beused for examination. For example, the surface area of each peak can beused to determine the number of pixels of a dot area and the surroundingbackground.

[0031] FIGS. 10A-10D and FIGS. 11A-11F show steps in a preferredembodiment of a method to evaluate an image file of a substrate as shownin FIG. 3 to locate the centres of dot areas 1 and to determine the sizeof the evaluation window 3 to be used in the above referenced methods.It is noted that this method of evaluating the complete image file canbe used in combination with any method of evaluating one dot area andthis method is therefore not restricted to the above described methodsor any other specific dot area evaluation method.

[0032]FIG. 10A shows the display of an image file of a substratecorresponding to the substrate in FIG. 3. Although this image fileclearly comprises noise, some possible areas of interest can berecognised as dot areas 1 in the display of the unprocessed pixel valuesof the pixels of the image file. For locating all possible areas ofinterest, the pixel values of the image file are processed in a low passfilter algorithm to determine a matrix of local mean values for allpixels of the image file. The thus obtained matrix of local mean valuesfor all pixels of the image file is shown in FIG. 10B. These local meanvalues are obtained by using a second window not shown having a sizepreferably between 25-50% of the evaluation window used to evaluate thesignal intensity of a dot area. This second window is slided along thepixels of the image file pixel by pixel. At each position of the secondwindow the mean value of all pixels of the image file within the secondwindow is taken as the local mean value of the pixel at the centre ofthe second window. By sliding the second window along the completesurface of the image file, a local mean value is determined for eachpixel of the image file. In this manner actually all high frequencyinformation of the image file is removed.

[0033] The matrix of the actual pixels values of the image file iscombined with the matrix of local mean values obtained in the abovedescribed manner to obtain a first matrix of high/low pixel values forall pixels of the image file. These high/low pixels values are eitherhigh or low depending on whether the actual pixel value is above ofbelow the corresponding local mean value of the pixel. This means thatif an actual pixel value is above the corresponding local mean value,the high/low pixel value of the first matrix will be high, whereas ifthe actual pixel value is below the corresponding local mean value, thehigh/low pixel value of the first matrix will be low. The thus obtainedfirst matrix is displayed in FIG. 10C.

[0034] The high/low pixel values of the first matrix are furtherprocessed in a median filter algorithm to obtain a second matrix ofmedian pixel values for all pixels of the image file. The thus obtainedmatrix of median pixel values is shown in FIG. 10D. According to thismedian filter algorithm the high/low pixel value of each pixel of thefirst matrix is compared with the high/low pixel values of the pixelsimmediately surrounding this pixel. The high/low pixel value is madeequal to the value of the majority of the high/low pixel values of thesesurrounding pixels and this new value is referred to as median pixelvalue.

[0035] As shown in FIG. 10D, the thus obtained matrix clearly shows allpossible areas of interest. A first rough estimate for the location ofthe centres of these possible areas of interest is obtained bydetermining the mean row values and mean column values row by row andcolumn by column, respectively. These mean row values and mean columnvalues are determined by adding row by row and column by column allmedian pixels values of the second matrix and dividing the thus obtainedvalue by the number of pixels. These mean values are shown at the bottomand right sides of the display of the image file in FIG. 10D as curves10 and 11. As it is known that a substrate with 6×4 possible areas ofinterest are used, the four highest mean row values and six highest meancolumn values are selected as estimates of row centre lines 12 andcolumn centre lines 13 of the possible areas of interest. Theintersections of these row and column centre lines 12, 13 are used asthe location of the centres 14 of all possible areas of interest.

[0036] FIGS. 11A-11F show further steps in the method to locate thecentres of possible areas of interest. FIG. 11A shows an evaluationwindow on an area of interest with the row centre line 12 and the columncentre line 13. In a further step it is determined whether theintersection 14 of lines 11, 12 is indeed in the centre of gravity 15 ofthe pixels of the area of interest. To this end the centre of gravity isdetermined from the median pixel values of the pixels of the secondmatrix within the evaluation window shown in FIG. 11A. If this centre ofgravity does not coincide with the intersection 14, the centre lines 12,13 are shifted such that their intersection coincides with the centre ofgravity 15 as shown in FIG. 11B.

[0037] To decide whether the pixels within the evaluation window havinga high value are indeed representing an area of interest or just randomnoise, i.e. the binding substance at this location of the substrate didnot bind with an analyte, the following steps are carried out. FIGS. 11Cand 11E show two possible areas of interest within an evaluation window.First, the surface of the pixels having a high value within theevaluation window of the second matrix is determined and from thissurface the radius of the area of interest is determined. Further theradius of this surface is determined from the circumference of thissurface. As shown in FIG. 11C, the pixels with a high value do notrepresent an exact circle, so that the radius determined from thecircumference of this area will be greater than the radius of an exactcircle 16 shown in FIG. 11C. However, the ratio of the two radii shouldbe less than a predetermined reference value, for example less than 2.FIG. 11D shows all pixels having a high value at the circumference ofthe surface of the pixels having a high value. The ratio of the radiusobtained from the circumference and the radius obtained from the surfaceis in this case 1.47. This means that an area of interest is present. InFIG. 11F, all pixels having a high value at the circumference of thesurface shown in FIG. 11 E are shown and the ratio of the “radius”obtained from the circumference and the radius obtained from the surfaceshown in FIG. 11E is in this case 4.47. This means that the evaluationwindow in case of FIG. 11E does not enclose and area of interest, i.e.an area of interest is absent.

[0038] As an alternative or as a further step to decide on the presenceor absence of an area of interest, it is also possible to take thecentre of gravity of a possible area of interest found as the centre ofan imaginary circle window having a surface corresponding to the surfaceof all pixels having a high value within the evaluation window as shownin FIG. 11. Thereafter this imaginary circle window can be moved withrespect to the centre of gravity to find a location wherein theimaginary circle window covers a predetermined number of pixels having ahigh value, for example at least 90% of all pixels. If such a locationcan not be found, an area of interest is absent. If such a location canbe found, an area of interest is present.

[0039] The size of the evaluation window used to examine possible areasof interest and to evaluate the signal intensity of an area of interestis determined such that the number of pixels with a high valuecorresponds with the number of pixels with a low value. This means thatin case of presence of a dot area 1, the number of dot pixels and thenumber of background pixels are equal, i.e. 50% of the total number ofpixels. Such a distribution will also be present, i.e. 50% of the pixelswithin the evaluation window above a mean value and 50% below a meanvalue, if only noise is present within the evaluation window. Using suchan evaluation window shows the advantage that fitting can be performedwith maximum reliability in the above described method for evaluation ofthe signal intensity of the dot area. Finally, if a user wants to movethe centre of a dot area or change the size of the evaluation window,the user can do so by means of a suitable user interface.

[0040] The invention is not restricted to the above-describedembodiments, which can be varied in a number of ways within the scope ofthe following claims.

1. Method for evaluating the signal intensity of at least one area of asubstrate embedded in a substrate surrounding with a backgroundintensity by means of a computer, characterized in that a scatterparameter of the substrate is determined, and in that a matrix (I_(S))of pixel intensities of pixels located within an evaluation windowenclosing the area and surrounding is determined, wherein the pixelintensities of the matrix (I_(S)) are used to obtain an evaluationhistogram of pixel intensities, said evaluation histogram showing twodistribution peaks, the first distribution peak with the lowestintensity corresponding with the surrounding pixels and the seconddistribution peak with the highest intensity corresponding with the areapixels, wherein a curve with two peaks is fitted on said evaluationhistogram, wherein the scatter parameter is used to correct forscattering either the matrix (I_(S)) or the curve, wherein the signalintensity of pixels in the area is determined by means of data obtainedfrom the curve fitted on said evaluation histogram.
 2. Method accordingto claim 1, wherein the matrix (I_(S)) of pixel intensities is firstprocessed with the scatter parameter to determine the deconvolution ofthe matrix (I_(S)) to obtain a matrix (I_(D)) of non-scattered pixelintensities, wherein the non-scattered pixel intensities are used toobtain the evaluation histogram, wherein a normal distribution curvedetermined by means of a noise parameter obtained from the pixelintensities of the matrix (I_(S)), is fitted on each of said twodistributions, wherein the mean value of the first fitted distributioncurve is taken as the mean pixel intensity of the background intensityand the mean value of the second fitted distribution curve is taken asthe mean pixel intensity of the area intensity, wherein the differenceof the mean values corresponds with the signal intensity of the pixelsin the area.
 3. Method according to claim 1, wherein a theoreticalscatter histogram of pixel intensities is determined using a theoreticalarea with predetermined signal intensity (I_(A)) and a surrounding withbackground intensity (I_(B)) located within an evaluation windowcorresponding with the evaluation window used to determine the values ofthe matrix (I_(S)), the scatter parameter and a noise parameter obtainedfrom the pixel intensities of the matrix (I_(S)), wherein saidevaluation histogram is made of the pixel intensities of the matrix(I_(S)) and the theoretical scatter histogram is fitted on saidevaluation histogram by varying at least the background and areaintensities of the theoretical scatter histogram, wherein the differencebetween the area intensity and background intensity of the fittedtheoretical histogram corresponds with the signal intensity of thepixels in the area to be evaluated.
 4. Method according to claim 3,wherein a table of normalized scatter response curves is made using anormalized theoretical area, a normalized predetermined intensity, andsurrounding without background intensity, wherein the scatter responsecurves each are determined along a radius from the centre of saidtheoretical area upto the edge of said evaluation window for each of anumber of scatter parameters for different substrates and substrateconditions, wherein a scatter response curve is selected from the tablein accordance with the substrate and substrate condition of thesubstrate used, wherein the selected scatter response curve is used todetermine the pixel intensities used to make a scatter histogram and thenoise parameter is convoluted with this scatter histogram to obtain thetheoretical scatter histogram.
 5. Method according to claim 2, 3 or 4,wherein said noise parameter is the standard deviation of the noisepresent in the pixel intensities of the matrix (I_(S)), wherein thenoise standard deviation is preferably determined by determiningdistribution of the difference between for example each two neighbouringpixels.
 6. Method according to any one of the preceding claims, whereinthe scatter parameter is determined by illuminating a side of thesubstrate in a direction parallel to said surface, wherein the pixelintensities along a line on the surface from said side in saidilluminating direction are determined to obtain an exponential scatterdecay function.
 7. Method according to claim 6, wherein the scatterparameter is corrected for signal intensity originating withoutscattering.
 8. Method according to any one of the preceding claims,wherein scatter parameters of different substrates for differentconditions of the substrate are determined and stored, wherein a scatterparameter is selected for use in the evaluation in accordance with thecondition of the substrate.
 9. Method according to any one of thepreceding claims, wherein the substrate has an array of areas, wherein amatrix of pixel intensities is determined for each area andcorresponding surrounding.
 10. Method according to claim 9, wherein thecomplete surface of the substrate is evaluated to locate the centre ofeach area and to determine the size of the evaluation window, whereinthe location of each area can be corrected through a user interface andthereafter the signal intensity of each area is evaluated.
 11. Methodfor locating possible areas of interest of a substrate embedded in asubstrate surrounding by means of a computer, characterized in that animage file of the substrate is processed in a low pass filter algorithmto determine a matrix of local mean values for all pixels of the imagefile, wherein the matrix of local mean values is combined with thematrix of actual pixel values of the image file to obtain a first matrixof high/low pixel values which are either high or low depending on theactual pixel value being above or below the corresponding local meanvalue, wherein the high/low pixel values of the first matrix are furtherprocessed in a median filter algorithm to obtain a second matrix ofmedian pixel values, wherein each median pixel value equals the majorityof the high/low pixel values of the first matrix, wherein the medianpixel values are processed row by row and column by column to determinethe mean row values and mean column values, respectively, wherein therows and columns with the highest mean row values and highest meancolumn values are selected as estimates of the row centre lines andcolumn centre lines of possible areas of interest, the intersections ofwhich are the centres of possible areas of interest.
 12. Methodaccording to claim 11, wherein the low pass filter algorithm todetermine the matrix of local mean values comprises using a secondwindow with a size enclosing an expected possible area of interest andits surrounding, sliding said second window along the image matrix pixelby pixel and taking, at each position of the second window, the meanvalue of all pixels within the second window as the local mean value ofeach pixel at the centre of the second window.
 13. Method according toclaim 11 or 12, wherein the median filter algorithm comprises comparingthe high/low pixel value of each pixel with the high/low pixel values ofthe surrounding pixels, wherein a high/low pixel value is made equal tothe majority of the high/low pixel values of the surrounding pixels. 14.Method according to claim 11, 12 or 13, wherein for each centre of apossible area of interest found a centre of gravity is determined fromthe median pixel values having a value high within the second window,wherein the centre of gravity is taken as the centre of a possible areaof interest.
 15. Method according to claim 14, wherein for each centreof a possible area of interest the radius of the possible area ofinterest is determined from the surface of the median pixel valueshaving a value high and from the circumference of this surface, whereinthe ratio of the two radii is determined to decide on the presence orabsence of an area of interest.
 16. Method according to claim 14 or 15,wherein the centre of gravity of a possible area of interest found istaken as the centre of an imaginary circle window having a surfacecorresponding to the surface of the median pixel values having a highvalue, wherein the imaginary circle window is moved with respect to thecentre of gravity to find a location covering a predetermined number ofthe pixels having a high value, wherein the decision on presence orabsence of an area of interest is taken in dependence on whether or notsuch a location is found.
 17. Method according to any of claims 11-16,wherein the size of an evaluation window for evaluating an area ofinterest is determined such that the number of pixels with high valuecorresponds with the number of pixels with low value.
 18. A computerprogram comprising computer program code means adapted to perform themethod of any one of the preceding claims when said program is run on acomputer.
 19. A computer program as claimed in claim 17 embodied on acomputer readable medium or in a file download able in a computer.